Large language models (LLMs) like ChatGPT are increasingly treated by the public as if theypossess subjective experience, with multiple studies probing the popular opinion on the matter. Thepresent study provides the first large-scale measurement of this phenomenon in Poland. Anationally distributed online survey panel (N = 1,736) rated ChatGPT’s capacity for consciousexperience on a 1–100 scale. "Most respondents (74%) attributed ChatGPT at least minimalconsciousness on the continuous rating scale, closely mirroring prior U.S. findings; because thisfigure is sensitive to question format, however, we conservatively estimate that more than 10% ofrespondents would affirm ChatGPT's status as an experiencer under a stricter, categorical framing.Small exploratory associations suggested that older and more highly educated participants wereslightly more skeptical, whereas right-leaning respondents attributed slightly more consciousness.A multivariate regression confirmed that these associations were not simply artifacts ofconfounding among demographic variables, though together they explained less than 3% of thevariance in consciousness attributions. These effects, though modest, indicate theoretically relevantvariation. Interpreting these results through Epley’s three-factor anthropomorphism model, thework argues that limited mechanistic understanding of LLMs, highly human-like behavior, andunmet social needs likely contribute to widespread phenomenal consciousness attribution. Givenethical concerns about over-trust, parasocial attachment, and responsibility misperception, thesebeliefs matter for AI deployment and governance. Our findings highlight the need for evidence-based educational strategies that improve public understanding of how LLMs work while allowingbeneficial use of the technology
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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